Special Issue: Artificial Intelligence and Engineering Design

Abstract Artificial Intelligence (AI) has had a strong presence in engineering design for decades, and while theory, methods, and tools for engineering design have advanced significantly during this time, many grand challenges remain. Modern advancements in AI, including new strategies for capturing, storing, and analyzing data, have the potential to revolutionize engineering design processes in a variety of ways. The purpose of this special issue is to consolidate recent research activities that utilize existing or new AI methods to advance engineering design knowledge and capabilities.

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Special Issue: Engineering applications of representations of function, Part 2

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060405059998 ◽

2005 ◽

Vol 19 (3) ◽

pp. 137-137 ◽

Cited By ~ 6

Author(s):

ROBERT B. STONE ◽

AMARESH CHAKRABARTI

Keyword(s):

Artificial Intelligence ◽

Engineering Design ◽

The State ◽

Functional Requirements ◽

Formal Representation ◽

Special Issue ◽

Product System ◽

Engineering Applications ◽

Research Communities ◽

The Creation

In engineering design, the end goal is the creation of an artifact, product, system, or process that fulfills some functional requirements at some desired level of performance. As such, knowledge of functionality is essential in a wide variety of tasks in engineering activities, including modeling, generation, modification, visualization, explanation, evaluation, diagnosis, and repair of these artifacts and processes. A formal representation of functionality is essential for supporting any of these activities on computers. The goal of Parts 1 and 2 of this Special Issue is to bring together the state of knowledge of representing functionality in engineering applications from both the engineering and the artificial intelligence (AI) research communities.

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Editorial Special Issue on Recent Advances in Artificial Intelligence for Smart Manufacturing u2013 Part II

Intelligent Automation & Soft Computing ◽

10.31209/2019.100000082 ◽

2019 ◽

pp. -1--1

Author(s):

Zheng Xu ◽

Qingyuan Zhou ◽

Zhiguo Yan

Keyword(s):

Artificial Intelligence ◽

Smart Manufacturing ◽

Special Issue ◽

Recent Advances ◽

Editorial Special Issue

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Special issue on artificial intelligence and machine learning for real-time image processing

Journal of Real-Time Image Processing ◽

10.1007/s11554-021-01157-0 ◽

2021 ◽

Author(s):

S. Balamurugan

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Image Processing ◽

Real Time ◽

Special Issue ◽

Real Time Image Processing ◽

Real Time Image ◽

Time Image

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Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application

Annals of Behavioral Medicine ◽

10.1093/abm/kaaa095 ◽

2020 ◽

Vol 54 (12) ◽

pp. 942-947

Author(s):

Pol Mac Aonghusa ◽

Susan Michie

Keyword(s):

Climate Change ◽

Artificial Intelligence ◽

Machine Learning ◽

Behavior Change ◽

Behavioral Science ◽

Lessons Learned ◽

Learning Approaches ◽

Intervention Evaluation ◽

Research Activities ◽

Behavior Change Interventions

Abstract Background Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices. Purposes By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP). Methods The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists. Results Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data. Conclusions AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.

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